Model-based programming of intelligent embedded systems and robotic space explorers

Programming complex embedded systems involves reasoning through intricate system interactions along lengthy paths between sensors, actuators, and control processors. This is a challenging, time-consuming, and error-prone process requiring significant interaction between engineers and software progra...

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Bibliographic Details
Published in:Proceedings of the IEEE 2003-01, Vol.91 (1), p.212-237
Main Authors: Williams, B.C., Ingham, M.D., Chung, S.H., Elliott, P.H.
Format: Article
Language:English
Subjects:
Applied sciences
Automation
Computer programs
Computer science
control theory
systems
Control systems
Control theory. Systems
Embedded system
Embedded systems
Exact sciences and technology
Intelligent actuators
Intelligent robots
Intelligent sensors
Intelligent systems
Manufacturing engineering
Mathematical models
Orbital robotics
Programming
Robot programming
Robot sensing systems
Robotics
Sensor systems
Software
Software engineering
Space exploration
Strategy
Studies
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Summary:Programming complex embedded systems involves reasoning through intricate system interactions along lengthy paths between sensors, actuators, and control processors. This is a challenging, time-consuming, and error-prone process requiring significant interaction between engineers and software programmers. Furthermore, the resulting code generally lacks modularity and robustness in the presence of failure. Model-based programming addresses these limitations, allowing engineers to program reactive systems by specifying high-level control strategies and by assembling commonsense models of the system hardware and software. In executing a control strategy, model-based executives reason about the models "on the fly," to track system state, diagnose faults, and perform reconfigurations. This paper develops the reactive model-based programming language (RMPL) and its executive, called Titan. RMPL provides the features of synchronous, reactive languages, with the added ability of reading and writing to state variables that are hidden within the physical plant being controlled. Titan executes an RMPL program using extensive component-based declarative models of the plant to track states, analyze anomalous situations, and generate novel control sequences. Within its reactive control loop, Titan employs propositional inference to deduce the system's current and desired states, and it employs model-based reactive planning to move the plant from the current to the desired state.
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2002.805828